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<div class="title">registration.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="preprocessor">#ifndef REGISTRATION_H</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="preprocessor">#define REGISTRATION_H</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160; </div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="preprocessor">#include &quot;typedefs.h&quot;</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160; </div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &lt;pcl/registration/ia_ransac.h&gt;</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &lt;pcl/registration/icp.h&gt;</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160; </div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment">/* Use SampleConsensusInitialAlignment to find a rough alignment from the source cloud to the target cloud by finding</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * correspondences between two sets of local features</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * Inputs:</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *   source_points</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *     The &quot;source&quot; points, i.e., the points that must be transformed to align with the target point cloud</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *   source_descriptors</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *     The local descriptors for each source point</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> *   target_points</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> *     The &quot;target&quot; points, i.e., the points to which the source point cloud will be aligned</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> *   target_descriptors</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> *     The local descriptors for each target point</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> *   min_sample_distance</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> *     The minimum distance between any two random samples</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> *   max_correspondence_distance</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> *     The </span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment"> *   nr_interations</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="comment"> *     The number of RANSAC iterations to perform</span></div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="comment"> * Return: A transformation matrix that will roughly align the points in source to the points in target</span></div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;Eigen::Matrix4f</div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;computeInitialAlignment (<span class="keyword">const</span> PointCloudPtr &amp; source_points, <span class="keyword">const</span> LocalDescriptorsPtr &amp; source_descriptors,</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;                         <span class="keyword">const</span> PointCloudPtr &amp; target_points, <span class="keyword">const</span> LocalDescriptorsPtr &amp; target_descriptors,</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;                         <span class="keywordtype">float</span> min_sample_distance, <span class="keywordtype">float</span> max_correspondence_distance, <span class="keywordtype">int</span> nr_iterations)</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;{</div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;  <a class="code" href="classpcl_1_1_sample_consensus_initial_alignment.html">pcl::SampleConsensusInitialAlignment&lt;PointT, PointT, LocalDescriptorT&gt;</a> sac_ia;</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_sample_consensus_initial_alignment.html#a6dff291eef83d6ad0bd63ccd3de7615b">setMinSampleDistance</a> (min_sample_distance);</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_registration.html#a65596dcc3cb5d2647857226fb3d999a5">setMaxCorrespondenceDistance</a> (max_correspondence_distance);</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_registration.html#a3844d186f7a99d15464368e0f25635ed">setMaximumIterations</a> (nr_iterations);</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;  </div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_registration.html#a6a111fe9b14dc3c65ca420b640e42de9">setInputCloud</a> (source_points);</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_sample_consensus_initial_alignment.html#aefc78c638aea7acebd0cbba4bf8a0d8c">setSourceFeatures</a> (source_descriptors);</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_registration.html#a4c4e69008295052913c76175797b99a9">setInputTarget</a> (target_points);</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_sample_consensus_initial_alignment.html#a8465edbc2a91fdd7fe8c44975ed7c658">setTargetFeatures</a> (target_descriptors);</div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud</a> registration_output;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  sac_ia.<a class="code" href="classpcl_1_1_registration.html#a96212303ca16b6d60020824086887c4f">align</a> (registration_output);</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160; </div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  <span class="keywordflow">return</span> (sac_ia.<a class="code" href="classpcl_1_1_registration.html#a1e68bd39ac943131dcbf1431f9afe3f3">getFinalTransformation</a> ());</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;}</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="comment">/* Use IterativeClosestPoint to find a precise alignment from the source cloud to the target cloud, </span></div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="comment"> * starting with an intial guess</span></div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="comment"> * Inputs:</span></div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="comment"> *   source_points</span></div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="comment"> *     The &quot;source&quot; points, i.e., the points that must be transformed to align with the target point cloud</span></div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="comment"> *   target_points</span></div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="comment"> *     The &quot;target&quot; points, i.e., the points to which the source point cloud will be aligned</span></div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="comment"> *   intial_alignment</span></div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="comment"> *     An initial estimate of the transformation matrix that aligns the source points to the target points</span></div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="comment"> *   max_correspondence_distance</span></div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="comment"> *     A threshold on the distance between any two corresponding points.  Any corresponding points that are further </span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="comment"> *     apart than this threshold will be ignored when computing the source-to-target transformation</span></div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="comment"> *   outlier_rejection_threshold</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="comment"> *     A threshold used to define outliers during RANSAC outlier rejection</span></div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="comment"> *   transformation_epsilon</span></div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="comment"> *     The smallest iterative transformation allowed before the algorithm is considered to have converged</span></div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="comment"> *   max_iterations</span></div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="comment"> *     The maximum number of ICP iterations to perform</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="comment"> * Return: A transformation matrix that will precisely align the points in source to the points in target</span></div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;Eigen::Matrix4f</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;refineAlignment (<span class="keyword">const</span> PointCloudPtr &amp; source_points, <span class="keyword">const</span> PointCloudPtr &amp; target_points, </div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;                 <span class="keyword">const</span> Eigen::Matrix4f &amp;initial_alignment, <span class="keywordtype">float</span> max_correspondence_distance,</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;                 <span class="keywordtype">float</span> outlier_rejection_threshold, <span class="keywordtype">float</span> transformation_epsilon, <span class="keywordtype">float</span> max_iterations)</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;{</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  <a class="code" href="classpcl_1_1_iterative_closest_point.html">pcl::IterativeClosestPoint&lt;PointT, PointT&gt;</a> icp;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#a65596dcc3cb5d2647857226fb3d999a5">setMaxCorrespondenceDistance</a> (max_correspondence_distance);</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#a64db6d25e2707a174dbad28f2484bffe">setRANSACOutlierRejectionThreshold</a> (outlier_rejection_threshold);</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#aec74ab878cca8d62fd1be9942685a8c1">setTransformationEpsilon</a> (transformation_epsilon);</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#a3844d186f7a99d15464368e0f25635ed">setMaximumIterations</a> (max_iterations);</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  PointCloudPtr source_points_transformed (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud</a>);</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a> (*source_points, *source_points_transformed, initial_alignment);</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#a6a111fe9b14dc3c65ca420b640e42de9">setInputCloud</a> (source_points_transformed);</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;  icp.<a class="code" href="classpcl_1_1_iterative_closest_point.html#a8876f743cc31471a975e950e66c179d4">setInputTarget</a> (target_points);</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160; </div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">PointCloud</a> registration_output;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  icp.<a class="code" href="classpcl_1_1_registration.html#a96212303ca16b6d60020824086887c4f">align</a> (registration_output);</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160; </div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  <span class="keywordflow">return</span> (icp.<a class="code" href="classpcl_1_1_registration.html#a1e68bd39ac943131dcbf1431f9afe3f3">getFinalTransformation</a> () * initial_alignment);</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;}</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160; </div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="ttc" id="aclasspcl_1_1_iterative_closest_point_html"><div class="ttname"><a href="classpcl_1_1_iterative_closest_point.html">pcl::IterativeClosestPoint</a></div><div class="ttdoc">IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm....</div><div class="ttdef"><b>Definition:</b> icp.h:95</div></div>
<div class="ttc" id="aclasspcl_1_1_iterative_closest_point_html_a8876f743cc31471a975e950e66c179d4"><div class="ttname"><a href="classpcl_1_1_iterative_closest_point.html#a8876f743cc31471a975e950e66c179d4">pcl::IterativeClosestPoint::setInputTarget</a></div><div class="ttdeci">virtual void setInputTarget(const PointCloudTargetConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input target (e.g., the point cloud that we want to align to the target)</div><div class="ttdef"><b>Definition:</b> icp.h:212</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a1e68bd39ac943131dcbf1431f9afe3f3"><div class="ttname"><a href="classpcl_1_1_registration.html#a1e68bd39ac943131dcbf1431f9afe3f3">pcl::Registration::getFinalTransformation</a></div><div class="ttdeci">Matrix4 getFinalTransformation()</div><div class="ttdoc">Get the final transformation matrix estimated by the registration method.</div><div class="ttdef"><b>Definition:</b> registration.h:275</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a3844d186f7a99d15464368e0f25635ed"><div class="ttname"><a href="classpcl_1_1_registration.html#a3844d186f7a99d15464368e0f25635ed">pcl::Registration::setMaximumIterations</a></div><div class="ttdeci">void setMaximumIterations(int nr_iterations)</div><div class="ttdoc">Set the maximum number of iterations the internal optimization should run for.</div><div class="ttdef"><b>Definition:</b> registration.h:285</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a4c4e69008295052913c76175797b99a9"><div class="ttname"><a href="classpcl_1_1_registration.html#a4c4e69008295052913c76175797b99a9">pcl::Registration::setInputTarget</a></div><div class="ttdeci">virtual void setInputTarget(const PointCloudTargetConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input target (e.g., the point cloud that we want to align the input source t...</div><div class="ttdef"><b>Definition:</b> registration.hpp:58</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a64db6d25e2707a174dbad28f2484bffe"><div class="ttname"><a href="classpcl_1_1_registration.html#a64db6d25e2707a174dbad28f2484bffe">pcl::Registration::setRANSACOutlierRejectionThreshold</a></div><div class="ttdeci">void setRANSACOutlierRejectionThreshold(double inlier_threshold)</div><div class="ttdoc">Set the inlier distance threshold for the internal RANSAC outlier rejection loop.</div><div class="ttdef"><b>Definition:</b> registration.h:309</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a65596dcc3cb5d2647857226fb3d999a5"><div class="ttname"><a href="classpcl_1_1_registration.html#a65596dcc3cb5d2647857226fb3d999a5">pcl::Registration::setMaxCorrespondenceDistance</a></div><div class="ttdeci">void setMaxCorrespondenceDistance(double distance_threshold)</div><div class="ttdoc">Set the maximum distance threshold between two correspondent points in source &lt;-&gt; target....</div><div class="ttdef"><b>Definition:</b> registration.h:321</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a6a111fe9b14dc3c65ca420b640e42de9"><div class="ttname"><a href="classpcl_1_1_registration.html#a6a111fe9b14dc3c65ca420b640e42de9">pcl::Registration::setInputCloud</a></div><div class="ttdeci">void setInputCloud(const PointCloudSourceConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input source (e.g., the point cloud that we want to align to the target)</div><div class="ttdef"><b>Definition:</b> registration.hpp:43</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a96212303ca16b6d60020824086887c4f"><div class="ttname"><a href="classpcl_1_1_registration.html#a96212303ca16b6d60020824086887c4f">pcl::Registration::align</a></div><div class="ttdeci">void align(PointCloudSource &amp;output)</div><div class="ttdoc">Call the registration algorithm which estimates the transformation and returns the transformed source...</div><div class="ttdef"><b>Definition:</b> registration.hpp:169</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_aec74ab878cca8d62fd1be9942685a8c1"><div class="ttname"><a href="classpcl_1_1_registration.html#aec74ab878cca8d62fd1be9942685a8c1">pcl::Registration::setTransformationEpsilon</a></div><div class="ttdeci">void setTransformationEpsilon(double epsilon)</div><div class="ttdoc">Set the transformation epsilon (maximum allowable difference between two consecutive transformations)...</div><div class="ttdef"><b>Definition:</b> registration.h:336</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_initial_alignment_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus_initial_alignment.html">pcl::SampleConsensusInitialAlignment</a></div><div class="ttdoc">SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in ...</div><div class="ttdef"><b>Definition:</b> ia_ransac.h:55</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_initial_alignment_html_a6dff291eef83d6ad0bd63ccd3de7615b"><div class="ttname"><a href="classpcl_1_1_sample_consensus_initial_alignment.html#a6dff291eef83d6ad0bd63ccd3de7615b">pcl::SampleConsensusInitialAlignment::setMinSampleDistance</a></div><div class="ttdeci">void setMinSampleDistance(float min_sample_distance)</div><div class="ttdoc">Set the minimum distances between samples</div><div class="ttdef"><b>Definition:</b> ia_ransac.h:171</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_initial_alignment_html_a8465edbc2a91fdd7fe8c44975ed7c658"><div class="ttname"><a href="classpcl_1_1_sample_consensus_initial_alignment.html#a8465edbc2a91fdd7fe8c44975ed7c658">pcl::SampleConsensusInitialAlignment::setTargetFeatures</a></div><div class="ttdeci">void setTargetFeatures(const FeatureCloudConstPtr &amp;features)</div><div class="ttdoc">Provide a boost shared pointer to the target point cloud's feature descriptors</div><div class="ttdef"><b>Definition:</b> ia_ransac.hpp:60</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_initial_alignment_html_aefc78c638aea7acebd0cbba4bf8a0d8c"><div class="ttname"><a href="classpcl_1_1_sample_consensus_initial_alignment.html#aefc78c638aea7acebd0cbba4bf8a0d8c">pcl::SampleConsensusInitialAlignment::setSourceFeatures</a></div><div class="ttdeci">void setSourceFeatures(const FeatureCloudConstPtr &amp;features)</div><div class="ttdoc">Provide a boost shared pointer to the source point cloud's feature descriptors</div><div class="ttdef"><b>Definition:</b> ia_ransac.hpp:48</div></div>
<div class="ttc" id="agroup__common_html_ga52d532f7f2b4d7bba78d13701d3a33d8"><div class="ttname"><a href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a></div><div class="ttdeci">void transformPointCloud(const pcl::PointCloud&lt; PointT &gt; &amp;cloud_in, pcl::PointCloud&lt; PointT &gt; &amp;cloud_out, const Eigen::Transform&lt; Scalar, 3, Eigen::Affine &gt; &amp;transform, bool copy_all_fields=true)</div><div class="ttdoc">Apply an affine transform defined by an Eigen Transform</div><div class="ttdef"><b>Definition:</b> transforms.hpp:42</div></div>
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